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    Comparison of Known Foods Weights with Image-Based Portion-Size Automated Estimation and Adolescents' Self-Reported Portion Size

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    Fulltext not available
    Authors
    Lee, C.
    Chae, J.
    Schap, R.
    Kerr, Deborah
    Delp, E.
    Ebert, D.
    Boushey, C.
    Date
    2012
    Type
    Journal Article
    
    Metadata
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    Citation
    Lee, C. and Chae, J. and Schap, R. and Kerr, D. and Delp, E. and Ebert, D. and Boushey, C. 2012. Comparison of Known Foods Weights with Image-Based Portion-Size Automated Estimation and Adolescents' Self-Reported Portion Size. Journal of Diabetes Science and Technology. 6 (2): pp. 428-434.
    Source Title
    Journal of Diabetes Science and Technology
    DOI
    10.1177/193229681200600231
    ISSN
    1932-2968
    School
    School of Public Health
    URI
    http://hdl.handle.net/20.500.11937/48240
    Collection
    • Curtin Research Publications
    Abstract

    Background: Diet is a critical element of diabetes self-management. An emerging area of research is the use of images for dietary records using mobile telephones with embedded cameras. These tools are being designed to reduce user burden and to improve accuracy of portion-size estimation through automation. The objectives of this study were to (1) assess the error of automatically determined portion weights compared to known portion weights of foods and (2) to compare the error between automation and human. Methods: Adolescents (n = 15) captured images of their eating occasions over a 24 h period. All foods and beverages served were weighed. Adolescents self-reported portion sizes for one meal. Image analysis was used to estimate portion weights. Data analysis compared known weights, automated weights, and self-reported portions. Results: For the 19 foods, the mean ratio of automated weight estimate to known weight ranged from 0.89 to 4.61, and 9 foods were within 0.80 to 1.20. The largest error was for lettuce and the most accurate was strawberry jam. The children were fairly accurate with portion estimates for two foods (sausage links, toast) using one type of estimation aid and two foods (sausage links, scrambled eggs) using another aid. The automated method was fairly accurate for two foods (sausage links, jam); however, the 95% confidence intervals for the automated estimates were consistently narrower than human estimates. Conclusions: The ability of humans to estimate portion sizes of foods remains a problem and a perceived burden. Errors in automated portion-size estimation can be systematically addressed while minimizing the burden on people. Future applications that take over the burden of these processes may translate to better diabetes self-management.

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